Özgül SALOR-DURNA

Proceedings Paper | 2023 | IEEE Industry Applications Society Annual Meeting

The research work presented in this paper proposes a method for modeling the behavior of the Electric Arc Furnace (EAF) currents for a tap-to-tap time based on the DFT amplitude histograms of the EAF current waves. The method is used to model the EAF current behavior separately for each phase of the EAF operation: boring, melting and refining. The model is verified by comparing the THD histograms and the flicker measurements of the original and modeled EAF current waveforms. The proposed model can be used as an EAF model in the simulation environment for various purposes before the installation of an EAF. The method has low computat . . .ional load compared to various other techniques, since it utilizes only the amplitude distribution parameters of the first 13 frequency components and the one-cycle signal representing the higher order harmonics. The model is novel in the sense that every time the EAF current signal is generated, a unique waveform reflecting the corresponding distributions is generated, which is compatible with the random behavior of the EAF operation. Keyword: EAF current; EAF modeling; electric arc furnace (EAF); power quality; steel makin More less

Özgül SALOR-DURNA

Proceedings Paper | 2023 | IEEE Industry Applications Society Annual Meeting

In this paper, utilization strategies for battery energy storage systems (BESS) are assessed in order to reduce the unscheduled power flows in the interconnection lines caused by multiple electric arc furnace operations in Turkey. Turkish electricity network is synchronously connected to the European Network of Transmission System Operators for Electricity (ENTSO-E) via 3 EHV transmission lines. Extensive amount of intermittent loads like electric arc furnaces (EAF) in the electricity network cause unscheduled power deviations at the intertie lines hence Area Control Error (ACE) performance decreases. Therefore, automatic generation . . . control (AGC) operated by the Transmission System Operator (TSO) requires more automatic Frequency Restoration Reserve (aFRR or Secondary Reserves) capacity to counteract the intermittent EAF loads. Nevertheless, the fast nature of EAF loads cannot be followed by the traditional generators participating in aFRR to keep the ACE between required performance limits. In order to mitigate the effects of these highly fluctuating loads on ACE, different AGC models incorporating BESS as secondary reserves in AGC is investigated. Time synchronized measurements collected for 17 major EAFs are used as disturbance to the power system. A two area dynamic simulation model comprising IEEE 14 bus and IEEE 118 bus models with AGC models are used to simulate the ACE variation between ENTSO-E and Turkey. Since batteries will be in high cyclic aging stress due to fast control signals, battery aging performance is also investigated. It is shown that BESS systems are effective in mitigating the unscheduled power flows due to the multiple EAF operations. Keyword: automatic generation controller (AGC); battery energy storage systems(BESS); electric arc furnace (EAF); state of charge (SOC); unscheduled power flows. dynamic simulatio More less

Özgül SALOR-DURNA

Proceedings Paper | 2021 | IEEE Industry Applications Society Annual Meeting

In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response time delays and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low pass filters . . .and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low pass Butterworth filter is used together with a linear FIR based prediction or long short term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with and LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06, 0.31, 0.99 prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5, 1.90, and 3.2 reconstruction errors for the above-mentioned methods More less

Özgül SALOR-DURNA

Proceedings Paper | 2022 | IEEE Industry Applications Society Annual Meeting

In this research work, a new method which determines the individual harmonic voltage contributions of the EAF plants supplied from a point of common coupling (PCC) to the PCC voltage is presented. EAFs are one of the most significant sources of the harmonics, especially the uncharacteristic ones, therefore it is important to be able to discriminate the amount of individual contributions from the feeders of a PCC supplying multiple EAFs. The proposed method uses the relationship derived between the correlation coefficient of the PCC voltage and the feeder current waveforms and the harmonic voltage contribution of each plant supplied . . .from the PCC. The main idea is based on the fact that harmonic voltage at the PCC is a result of the additive effect of voltage drops on the source side impedance of the power system caused by the individual feeder currents. After computing the Pearson correlation coefficients at each harmonic frequency using 10-cycle synchronized feeder current and PCC voltage waveforms, harmonic voltage contribution of the related feeder is obtained using the proposed method. This procedure can be repeatedly used to obtain the contribution of each EAF plant at each frequency component. Field measurements from a PCC supplying multiple EAF plants are used to verify the results and the performance is compared with the previously proposed methods. With a specified source side impedance by the utility, the proposed approach is a fast method of obtaining the harmonic responsibilities, since no real-time impedance measurements are required and no need for the measurements of the other feeders to compute the contribution of a specific feeder in contrast to some other methods. The proposed method can be easily adapted as a real time harmonic contribution detection tool for the power quality analyzers, all of which have synchronized voltage and current waveform measurements. Keyword: electric arc furnace; electrical power quality; harmonic voltage contribution; point of common coupling; power qualit More less

Özgül SALOR-DURNA

Proceedings Paper | 2021 | IEEE Industry Applications Society Annual Meeting

In this paper, the problem of detecting the harmonic responsibility of iron and steel (I&S) plants, which are supplied from a point of common coupling (PCC) is addressed. A new harmonic responsibility measure, which does not require the instantaneous impedance measurements, is proposed to present the amount of harmonic responsibility of each plant supplied from the PCC. The algorithm is based primarily on the correlation of voltage and current signals which are measured with a time-synchronized manner at the PCC. The proposed method is first verified using both synthetic data generated in PSCAD/EMTDC simulation environment and the f . . .ield data. Then measurements of voltage and current waveforms via mobile Power Quality (PQ) measurement systems from one of the PCCs of the electricity transmission system are used for the verification of the proposed algorithm. The results of the method have been compared with those of the existing methods in the literature and it has been shown that the harmonic responsibility of each I&S plant can be determined successfully for each harmonic order. The advantage of the proposed method is that there is no more need for online system impedance measurements or in-plant measurements. The algorithm can be applied to power quality monitoring systems, active power filters, synchronous static compensators (STATCOMs) and other compensation systems to reduce and control the distortion effect of current harmonics and also interharmonics, if required, which are casued by I&S plants supplied from the PCCs of the electricty transmission system More less

Özgül SALOR-DURNA

Proceedings Paper | 2020 | IEEE Industry Applications Society Annual Meeting

In this paper, a method to generate large amounts of Electric Arc Furnace (EAF) currents with harmonics simulating the actual EAF operation characteristics to be used with deep learning (DL) applications of harmonic estimation is investigated. For this purpose, the behavior of the EAF current harmonics is examined in statistical terms using the field data collected at a transformer substation supplying an EAF plant. Then, a significantly larger amount of EAF current data is generated using the statistics mimicking the real EAF behavior to train the DL-based harmonic estimator. The outcomes of the research work presented in this pape . . .r are two-fold: (i) DL-based method is used to extract phase and amplitude information of the harmonics of the EAF currents using the waveform directly, without computing any time- or frequency-domain features during the estimation process, which helps reduce the processing time, (ii) EAF current data with realistic amounts of time-varying harmonics based on the statistics obtained from a tap-to-tap time of the EAF currents is generated, hence a detailed statistical analysis of the EAF current spectrum is achieved. The method proposed can be used to eliminate the uncharacteristic harmonics of the EAF currents, since it can provide fast and accurate phase and amplitude estimates of the harmonics, serving the need for active power filters in the electricity system More less